Policy Gradient Methods for Reinforcement Learning with ...
of expected reward with respect to the policy parameters. Williams's REINFORCE method and actor-critic methods are examples of this approach. Our main new result is to show that the gradient can be written in a form suitable for estimation from experience aided by an approximate action-value or advantage function. Using this
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Bootstrap Your Own Latent A New Approach to Self ...
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Spatial Transformer Networks - NeurIPS
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proceedings.neurips.ccapproximately invariant to local perturbations along the manifold. The idea of manifold learning ... We show for the first time how variational inference can be brought to bear upon the prob- ... probabilities are formed by a non-linear transformation, with parameters , of a set of latent vari-ables z. This non-linear transformation is ...
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proceedings.neurips.ccpseudo-labels to learn visual representations. This method scales to large uncurated dataset and can be used for pre-training of supervised networks [7]. However, their formulation is not principled and recently, Asano et al. [2] show how to cast the pseudo-label assignment problem as an instance of the optimal transport problem.
PyTorch: An Imperative Style, High-Performance Deep ...
proceedings.neurips.ccFacebook AI Research benoitsteiner@fb.com Lu Fang Facebook lufang@fb.com Junjie Bai Facebook jbai@fb.com Soumith Chintala Facebook AI Research soumith@gmail.com Abstract Deep learning frameworks have often focused on either usability or speed, but not both. PyTorch is a machine learning library that shows that these two goals
Visualizing the Loss Landscape of Neural Nets
proceedings.neurips.cctask that is hard in theory, but sometimes easy in practice. Despite the NP-hardness of training general neural loss functions [3], simple gradient methods often find global minimizers (parameter configurations with zero or near-zero training loss), even when data and labels are randomized before training [43].
InfoGAN: Interpretable Representation Learning by ...
proceedings.neurips.ccof the digit (0-9), and chose to have two additional continuous variables that represent the digit’s angle and thickness of the digit’s stroke. It would be useful if we could recover these concepts without any supervision, by simply specifying that an MNIST digit is generated by an 1-of-10 variable and two continuous variables.
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![arXiv:1911.08265v2 [cs.LG] 21 Feb 2020](/cache/preview/5/6/9/2/b/4/2/b/thumb-5692b42bda6f4e9a1e57c6cc4b99a074.jpg)
arXiv:1911.08265v2 [cs.LG] 21 Feb 2020
arxiv.orgThe parameters of the representation, dynamics and prediction functions are jointly trained, end-to-end by backpropagation-through-time, to predict three quantities: the policy pk ˇˇ t+k, value function v k ˇz t+k, and reward r t+k ˇu t+k, where z t+kis a sample return: either the final reward (board games) or n-step return (Atari).